Chandan Singh

Senior Researcher

À propos

👋Hello👋! I work in the deep learning group on interpretable machine learning with the broad goal of improving science & medicine with data. For details, see my personal website or my google scholar. Here’s what I’m excited about these days:

🔎 Interpretability. I’m interested in rethinking interpretability in the context of LLMs

augmented imodels (opens in new tab) – use LLMs to build a transparent model
imodels (opens in new tab) – build interpretable models in the style of scikit-learn
explanation penalization (opens in new tab) – regularize explanations to align models with prior knowledge
adaptive wavelet distillation (opens in new tab) – replace neural nets with simple, performant wavelet models

🚗 LLM steering. Interpretability tools can provide ways to better guide and use LLMs

tree prompting (opens in new tab) – improve black-box few-shot text classification with decision trees
attention steering (opens in new tab) – guide LLMs by emphasizing specific input spans
interpretable autoprompting (opens in new tab) – automatically find fluent natural-language prompts

🧠 Neuroscience. Since joining MSR, I have been focused on leveraging LLMs to understand how the human brain represents language (using fMRI in collaboration with the Huth lab (opens in new tab) at UT Austin).

explanation-mediated validation (opens in new tab) – build and test fMRI explanations using LLM-generated stimuli
qa embeddings (opens in new tab) – build interpretable fMRI encoding models by asking yes/no questions to LLMs
summarize & score explanations (opens in new tab) – generate natural-language explanations of fMRI encoding models

💊 Healthcare. I’m also actively working on how we can improve clinical decision instruments by using the information contained across various sources in the medical literature (in collaboration with many folks including Dr. Aaron Kornblith at UCSF and the MSR Health Futures team).

clinical self-verification (opens in new tab) – self-verification improves performance and interpretability of clinical information extraction
clinical rule vetting (opens in new tab) – stress testing a clinical decision instrument performance for intra-abdominal injury

My PhD at UC Berkeley (advised by Bin Yu (opens in new tab)) focused on working with scientists and doctors to develop interpretations for scientific domains.

Internships / collaborations

If you want to chat about research (or are interested in interning at MSR), feel free to reach out over email!

Previously, I’ve been lucky to help mentor some wonderful students:

Recent publications

Explaining Patterns in Data with Language Models via Interpretable Autoprompting

Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learned ability to find and explain patterns in data. Specifically, given a pre-trained…